Abstract Photovoltaic (PV) system as a vital element in the utilize of solar energy, its optimization, control, and simulation are significant. The performance of the PV system is mainly influenced by its model parameters that are varying and unavailable, thus identifying these model parameters is always desired. However, accurate and robust parameters estimation of PV models brings great challenges to the existing methods, since the complicated characteristics when estimating the parameters. Hence, to efficiently provide accurate parameters for the PV model, this study develops a self-adaptive ensemble-based differential evolution algorithm. Three different mutation strategies with different properties are combined into two groups for updating each individual. Furthermore, in order to make the best of different mutation strategies, a self-adaptive scheme is suggested to equilibrate population diversity and convergence, by adjusting the proportion of the mutation strategies used in the population. To evaluate the performance of SEDE, it is used to obtain the parameters of three PV models and compared with other well-established algorithms. Systematic comparison results indicate that SEDE is capable of estimating the model parameters with higher efficiency.

中文翻译：

---

基于自适应集成差分演化的太阳能光伏模型参数估计

摘要 光伏（PV）系统作为太阳能利用的重要组成部分，其优化、控制和仿真具有重要意义。光伏系统的性能主要受其可变且不可用的模型参数的影响，因此始终需要识别这些模型参数。然而，由于估计参数时的复杂特性，PV模型的准确和稳健的参数估计给现有方法带来了巨大挑战。因此，为了有效地为 PV 模型提供准确的参数，本研究开发了一种基于自适应集成的差分进化算法。将具有不同属性的三种不同的变异策略组合成两组以更新每个个体。此外，为了充分利用不同的突变策略，提出了一种自适应方案，通过调整种群中使用的突变策略的比例来平衡种群多样性和收敛性。为了评估 SEDE 的性能，它被用来获得三个 PV 模型的参数，并与其他完善的算法进行比较。系统比较结果表明，SEDE 能够更有效地估计模型参数。